To support the open source software movement, JMLR MLOSS publishes contributions related to implementations of non-trivial machine learning algorithms, toolboxes or even languages for scientific computing.

Sep 30, 2020 · Model-based learning is the typical approach for most non-trivial Machine Learning applications, since very complex problems can often be approximated more efficiently with a model instead of instance similarity comparison.

May 22, 2024 · This article highlights the top 10 machine learning algorithms that every data analyst should be familiar with, along with their applications and benefits in data analysis. 1. Linear...

Jan 1, 2022 · As a part of this study, we examine how accurate different classification algorithms are on diverse datasets. On five different datasets, four classification models are compared: Decision tree, SVM, Naive Bayesian, and K-nearest neighbor. The Naive Bayesian algorithm is proven to be the most effective among other algorithms.

Aug 7, 2020 · In this article, we looked at how machine learning can automate and scale data analysis. We summarized a few important machine-learning algorithms and saw their real-life applications.

Mar 22, 2021 · In this section, we discuss various machine learning algorithms that include classification analysis, regression analysis, data clustering, association rule learning, feature engineering for dimensionality reduction, as well as deep learning methods.

Distance function is a main metrics of measuring the affinity between two data points in machine learning. Extant distance functions often provide unreachable distance values in real...

Mar 10, 2024 · Unlock the secrets of effective data analysis by exploring the core machine learning algorithms that drive insights and decision-making.

In many machine learning settings, collected data may contain many irrelevant features together with relevant features (e.g., DNA sequences and big data), and the efficient techniques for selecting relevant features are widely required.

Master’s dissertation aims to identify the relationship between trivial and non-trivial refactorings, in addition to proposing a metric that evaluates the triviality of implementing refactorings. Initially, we use supervised learning classifier models to examine the impact of trivial refactorings on the prediction of non-trivial ones.